"""simple docstring"""







import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
           MODEL_FOR_QUESTION_ANSWERING_MAPPING,
           MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
           MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
           LayoutLMvaConfig,
           LayoutLMvaForQuestionAnswering,
           LayoutLMvaForSequenceClassification,
           LayoutLMvaForTokenClassification,
           LayoutLMvaModel,
       )
       from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
       from PIL import Image

       from transformers import LayoutLMvaImageProcessor







class 			UpperCamelCase						:
   def __init__(     self	,__UpperCamelCase	,__UpperCamelCase=2	,__UpperCamelCase=3	,__UpperCamelCase=4	,__UpperCamelCase=2	,__UpperCamelCase=7	,__UpperCamelCase=True	,__UpperCamelCase=True	,__UpperCamelCase=True	,__UpperCamelCase=True	,__UpperCamelCase=99	,__UpperCamelCase=36	,__UpperCamelCase=3	,__UpperCamelCase=4	,__UpperCamelCase=37	,__UpperCamelCase="gelu"	,__UpperCamelCase=0.1	,__UpperCamelCase=0.1	,__UpperCamelCase=512	,__UpperCamelCase=16	,__UpperCamelCase=2	,__UpperCamelCase=0.02	,__UpperCamelCase=6	,__UpperCamelCase=6	,__UpperCamelCase=3	,__UpperCamelCase=4	,__UpperCamelCase=None	,__UpperCamelCase=1000	,)      -> Union[str, Any]:


        '''simple docstring'''


        lowercase_      :							List[Any]						=      parent
        lowercase_      :							Optional[int]						=      batch_size
        lowercase_      :							List[str]						=      num_channels
        lowercase_      :							Tuple						=      image_size
        lowercase_      :							int						=      patch_size
        lowercase_      :							Optional[Any]						=      text_seq_length
        lowercase_      :							List[Any]						=      is_training
        lowercase_      :							str						=      use_input_mask
        lowercase_      :							Dict						=      use_token_type_ids
        lowercase_      :							Optional[int]						=      use_labels
        lowercase_      :							Tuple						=      vocab_size
        lowercase_      :							Any						=      hidden_size
        lowercase_      :							str						=      num_hidden_layers
        lowercase_      :							Optional[int]						=      num_attention_heads
        lowercase_      :							Any						=      intermediate_size
        lowercase_      :							Union[str, Any]						=      hidden_act
        lowercase_      :							Any						=      hidden_dropout_prob
        lowercase_      :							Optional[Any]						=      attention_probs_dropout_prob
        lowercase_      :							Union[str, Any]						=      max_position_embeddings
        lowercase_      :							int						=      type_vocab_size
        lowercase_      :							List[Any]						=      type_sequence_label_size
        lowercase_      :							List[str]						=      initializer_range
        lowercase_      :							List[Any]						=      coordinate_size
        lowercase_      :							Optional[int]						=      shape_size
        lowercase_      :							int						=      num_labels
        lowercase_      :							Tuple						=      num_choices
        lowercase_      :							Any						=      scope
        lowercase_      :							Union[str, Any]						=      range_bbox

        # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
        lowercase_      :							Union[str, Any]						=      text_seq_length
        lowercase_      :							Optional[int]						=      (image_size // patch_size) ** 2 + 1
        lowercase_      :							int						=      self.text_seq_length + self.image_seq_length
   def      _UpperCAmelCase  (     self     )      -> List[Any]:


        '''simple docstring'''


        lowercase_      :							List[str]						=      ids_tensor([self.batch_size, self.text_seq_length]	,self.vocab_size     )

        lowercase_      :							Dict						=      ids_tensor([self.batch_size, self.text_seq_length, 4]	,self.range_bbox     )
        # Ensure that bbox is legal
        for i in range(bbox.shape[0]     ):
             for j in range(bbox.shape[1]     ):
                  if bbox[i, j, 3] < bbox[i, j, 1]:
                       lowercase_      :							Union[str, Any]						=      bbox[i, j, 3]
                       lowercase_      :							List[Any]						=      bbox[i, j, 1]
                       lowercase_      :							Dict						=      t
                  if bbox[i, j, 2] < bbox[i, j, 0]:
                       lowercase_      :							int						=      bbox[i, j, 2]
                       lowercase_      :							int						=      bbox[i, j, 0]
                       lowercase_      :							List[Any]						=      t

        lowercase_      :							List[Any]						=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

        lowercase_      :							List[str]						=      None
        if self.use_input_mask:
             lowercase_      :							Optional[Any]						=      random_attention_mask([self.batch_size, self.text_seq_length]     )

        lowercase_      :							Any						=      None
        if self.use_token_type_ids:
             lowercase_      :							str						=      ids_tensor([self.batch_size, self.text_seq_length]	,self.type_vocab_size     )

        lowercase_      :							Optional[int]						=      None
        lowercase_      :							List[str]						=      None
        if self.use_labels:
             lowercase_      :							List[Any]						=      ids_tensor([self.batch_size]	,self.type_sequence_label_size     )
             lowercase_      :							List[Any]						=      ids_tensor([self.batch_size, self.text_seq_length]	,self.num_labels     )

        lowercase_      :							List[Any]						=      LayoutLMvaConfig(
            vocab_size=self.vocab_size	,hidden_size=self.hidden_size	,num_hidden_layers=self.num_hidden_layers	,num_attention_heads=self.num_attention_heads	,intermediate_size=self.intermediate_size	,hidden_act=self.hidden_act	,hidden_dropout_prob=self.hidden_dropout_prob	,attention_probs_dropout_prob=self.attention_probs_dropout_prob	,max_position_embeddings=self.max_position_embeddings	,type_vocab_size=self.type_vocab_size	,initializer_range=self.initializer_range	,coordinate_size=self.coordinate_size	,shape_size=self.shape_size	,input_size=self.image_size	,patch_size=self.patch_size	,)

        return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
   def      _UpperCAmelCase  (     self	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase     )      -> Any:


        '''simple docstring'''


        lowercase_      :							List[str]						=      LayoutLMvaModel(config=UpperCAmelCase_     )
        model.to(UpperCAmelCase_     )
        model.eval()

        # text + image
        lowercase_      :							Dict						=      model(UpperCAmelCase_	,pixel_values=UpperCAmelCase_     )
        lowercase_      :							str						=      model(
            UpperCAmelCase_	,bbox=UpperCAmelCase_	,pixel_values=UpperCAmelCase_	,attention_mask=UpperCAmelCase_	,token_type_ids=UpperCAmelCase_     )
        lowercase_      :							Any						=      model(UpperCAmelCase_	,bbox=UpperCAmelCase_	,pixel_values=UpperCAmelCase_	,token_type_ids=UpperCAmelCase_     )
        lowercase_      :							str						=      model(UpperCAmelCase_	,bbox=UpperCAmelCase_	,pixel_values=UpperCAmelCase_     )

        self.parent.assertEqual(result.last_hidden_state.shape	,(self.batch_size, self.seq_length, self.hidden_size)     )

        # text only
        lowercase_      :							Dict						=      model(UpperCAmelCase_     )
        self.parent.assertEqual(
            result.last_hidden_state.shape	,(self.batch_size, self.text_seq_length, self.hidden_size)     )

        # image only
        lowercase_      :							List[Any]						=      model(pixel_values=UpperCAmelCase_     )
        self.parent.assertEqual(
            result.last_hidden_state.shape	,(self.batch_size, self.image_seq_length, self.hidden_size)     )
   def      _UpperCAmelCase  (     self	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase     )      -> Any:


        '''simple docstring'''


        lowercase_      :							Any						=      self.num_labels
        lowercase_      :							Optional[int]						=      LayoutLMvaForSequenceClassification(UpperCAmelCase_     )
        model.to(UpperCAmelCase_     )
        model.eval()
        lowercase_      :							str						=      model(
            UpperCAmelCase_	,bbox=UpperCAmelCase_	,pixel_values=UpperCAmelCase_	,attention_mask=UpperCAmelCase_	,token_type_ids=UpperCAmelCase_	,labels=UpperCAmelCase_	,)
        self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.num_labels)     )
   def      _UpperCAmelCase  (     self	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase     )      -> Dict:


        '''simple docstring'''


        lowercase_      :							str						=      self.num_labels
        lowercase_      :							int						=      LayoutLMvaForTokenClassification(config=UpperCAmelCase_     )
        model.to(UpperCAmelCase_     )
        model.eval()
        lowercase_      :							Dict						=      model(
            UpperCAmelCase_	,bbox=UpperCAmelCase_	,pixel_values=UpperCAmelCase_	,attention_mask=UpperCAmelCase_	,token_type_ids=UpperCAmelCase_	,labels=UpperCAmelCase_	,)
        self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.text_seq_length, self.num_labels)     )
   def      _UpperCAmelCase  (     self	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase     )      -> str:


        '''simple docstring'''


        lowercase_      :							Tuple						=      LayoutLMvaForQuestionAnswering(config=UpperCAmelCase_     )
        model.to(UpperCAmelCase_     )
        model.eval()
        lowercase_      :							Tuple						=      model(
            UpperCAmelCase_	,bbox=UpperCAmelCase_	,pixel_values=UpperCAmelCase_	,attention_mask=UpperCAmelCase_	,token_type_ids=UpperCAmelCase_	,start_positions=UpperCAmelCase_	,end_positions=UpperCAmelCase_	,)
        self.parent.assertEqual(result.start_logits.shape	,(self.batch_size, self.seq_length)     )
        self.parent.assertEqual(result.end_logits.shape	,(self.batch_size, self.seq_length)     )







   def      _UpperCAmelCase  (     self     )      -> Optional[Any]:


        '''simple docstring'''


        lowercase_      :							Union[str, Any]						=      self.prepare_config_and_inputs()
        (
            (
            lowercase_							
        )				,    (
            lowercase_							
        )				,    (
            lowercase_							
        )				,    (
            lowercase_							
        )				,    (
            lowercase_							
        )				,    (
            lowercase_							
        )				,    (
            lowercase_							
        )				,    (
            lowercase_							
        )				,    							
        )      :							Tuple						=      config_and_inputs
        lowercase_      :							Tuple						=      {
            'input_ids': input_ids,
            'bbox': bbox,
            'pixel_values': pixel_values,
            'token_type_ids': token_type_ids,
            'attention_mask': input_mask,
        }
        return config, inputs_dict







@require_torch
class 			UpperCamelCase						(     _lowerCAmelCase		, _lowerCAmelCase		, unittest.TestCase    ):
   lowercase				=    False
   lowercase				=    False
   lowercase				=    False

   lowercase				=    (
       (
           LayoutLMvaModel,
           LayoutLMvaForSequenceClassification,
           LayoutLMvaForTokenClassification,
           LayoutLMvaForQuestionAnswering,
       )
       if is_torch_available()
       else ()
   )
   lowercase				=    (
       {'''document-question-answering''': LayoutLMvaForQuestionAnswering, '''feature-extraction''': LayoutLMvaModel}
       if is_torch_available()
       else {}
   )
   def      _UpperCAmelCase  (     self	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase     )      -> Dict:


        '''simple docstring'''


        return True
   def      _UpperCAmelCase  (     self     )      -> Union[str, Any]:


        '''simple docstring'''


        lowercase_      :							Dict						=      LayoutLMvaModelTester(self     )
        lowercase_      :							List[str]						=      ConfigTester(self	,config_class=UpperCAmelCase_	,hidden_size=37     )
   def      _UpperCAmelCase  (     self	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase=False     )      -> Any:


        '''simple docstring'''


        lowercase_      :							Union[str, Any]						=      copy.deepcopy(UpperCAmelCase_     )
        if model_class in get_values(UpperCAmelCase_     ):
             lowercase_      :							Dict						=      {
                 k: v.unsqueeze(1     ).expand(-1	,self.model_tester.num_choices	,-1     ).contiguous()
                 if isinstance(UpperCAmelCase_	,torch.Tensor     ) and v.ndim > 1
                 else v
                 for k, v in inputs_dict.items()
             }
        if return_labels:
             if model_class in get_values(UpperCAmelCase_     ):
                  lowercase_      :							Union[str, Any]						=      torch.ones(self.model_tester.batch_size	,dtype=torch.long	,device=UpperCAmelCase_     )
             elif model_class in get_values(UpperCAmelCase_     ):
                  lowercase_      :							Tuple						=      torch.zeros(
                      self.model_tester.batch_size	,dtype=torch.long	,device=UpperCAmelCase_     )
                  lowercase_      :							Any						=      torch.zeros(
                      self.model_tester.batch_size	,dtype=torch.long	,device=UpperCAmelCase_     )
             elif model_class in [
                 *get_values(UpperCAmelCase_     ),
             ]:
                  lowercase_      :							Any						=      torch.zeros(
                      self.model_tester.batch_size	,dtype=torch.long	,device=UpperCAmelCase_     )
             elif model_class in [
                 *get_values(UpperCAmelCase_     ),
             ]:
                  lowercase_      :							Tuple						=      torch.zeros(
                      (self.model_tester.batch_size, self.model_tester.text_seq_length)	,dtype=torch.long	,device=UpperCAmelCase_	,)

        return inputs_dict
   def      _UpperCAmelCase  (     self     )      -> Union[str, Any]:


        '''simple docstring'''


        self.config_tester.run_common_tests()
   def      _UpperCAmelCase  (     self     )      -> Any:


        '''simple docstring'''


        lowercase_      :							Union[str, Any]						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCAmelCase_     )
   def      _UpperCAmelCase  (     self     )      -> Union[str, Any]:


        '''simple docstring'''


        lowercase_      :							Any						=      self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
             lowercase_      :							Dict						=      type
             self.model_tester.create_and_check_model(*UpperCAmelCase_     )
   def      _UpperCAmelCase  (     self     )      -> int:


        '''simple docstring'''


        lowercase_      :							List[Any]						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase_     )
   def      _UpperCAmelCase  (     self     )      -> Dict:


        '''simple docstring'''


        lowercase_      :							str						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase_     )
   def      _UpperCAmelCase  (     self     )      -> List[str]:


        '''simple docstring'''


        lowercase_      :							int						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase_     )







   @slow
   def      _UpperCAmelCase  (     self     )      -> int:


        '''simple docstring'''


        for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_      :							List[str]						=      LayoutLMvaModel.from_pretrained(UpperCAmelCase_     )
             self.assertIsNotNone(UpperCAmelCase_     )






def      lowercase__(			):
     lowercase_      :							Dict						=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
     return image







@require_torch
class 			UpperCamelCase						(     unittest.TestCase    ):
   @cached_property
   def      _UpperCAmelCase  (     self     )      -> Union[str, Any]:


        '''simple docstring'''


        return LayoutLMvaImageProcessor(apply_ocr=UpperCAmelCase_     ) if is_vision_available() else None







   @slow
   def      _UpperCAmelCase  (     self     )      -> Optional[int]:


        '''simple docstring'''


        lowercase_      :							List[str]						=      LayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'     ).to(UpperCAmelCase_     )

        lowercase_      :							Any						=      self.default_image_processor
        lowercase_      :							List[str]						=      prepare_img()
        lowercase_      :							Optional[Any]						=      image_processor(images=UpperCAmelCase_	,return_tensors='pt'     ).pixel_values.to(UpperCAmelCase_     )

        lowercase_      :							int						=      torch.tensor([[1, 2]]     )
        lowercase_      :							str						=      torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]     ).unsqueeze(0     )

        # forward pass
        lowercase_      :							Dict						=      model(
            input_ids=input_ids.to(UpperCAmelCase_     )	,bbox=bbox.to(UpperCAmelCase_     )	,pixel_values=pixel_values.to(UpperCAmelCase_     )	,)

        # verify the logits
        lowercase_      :							List[Any]						=      torch.Size((1, 199, 768)     )
        self.assertEqual(outputs.last_hidden_state.shape	,UpperCAmelCase_     )

        lowercase_      :							List[str]						=      torch.tensor(
            [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]     ).to(UpperCAmelCase_     )

        self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]	,UpperCAmelCase_	,atol=1e-4     )     )



"""simple docstring"""


from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
      import datasets



class 	__a	(   _lowerCAmelCase						):






       @slow
       @require_torch
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	Dict:







              """simple docstring"""
              UpperCamelCase       			=		EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny"							,   "prajjwal1/bert-tiny"      )
              UpperCamelCase       			=		BertTokenizer.from_pretrained("bert-base-uncased"      )

              UpperCamelCase       			=		bertabert.config.encoder.vocab_size
              UpperCamelCase       			=		tokenizer.sep_token_id
              UpperCamelCase       			=		tokenizer.cls_token_id
              UpperCamelCase       			=		128

              UpperCamelCase       			=		datasets.load_dataset("cnn_dailymail"							,   "3.0.0"							,   split="train[:1%]"      )
              UpperCamelCase       			=		datasets.load_dataset("cnn_dailymail"							,   "3.0.0"							,   split="validation[:1%]"      )

              UpperCamelCase       			=		train_dataset.select(range(32      )      )
              UpperCamelCase       			=		val_dataset.select(range(16      )      )

              UpperCamelCase       			=		4

              def _map_to_encoder_decoder_inputs(UpperCAmelCase_			:				Any      ):
                     # Tokenizer will automatically set [BOS] <text> [EOS]
                     UpperCamelCase       			=		tokenizer(batch["article"]							,   padding="max_length"							,   truncation=UpperCAmelCase_							,   max_length=512      )
                     UpperCamelCase       			=		tokenizer(batch["highlights"]							,   padding="max_length"							,   truncation=UpperCAmelCase_							,   max_length=128      )
                     UpperCamelCase       			=		inputs.input_ids
                     UpperCamelCase       			=		inputs.attention_mask

                     UpperCamelCase       			=		outputs.input_ids
                     UpperCamelCase       			=		outputs.input_ids.copy()
                     UpperCamelCase       			=		[
                         [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
                     ]
                     UpperCamelCase       			=		outputs.attention_mask

                     assert all(len(UpperCAmelCase_      ) == 512 for x in inputs.input_ids      )
                     assert all(len(UpperCAmelCase_      ) == 128 for x in outputs.input_ids      )

                     return batch

              def _compute_metrics(UpperCAmelCase_			:				Any      ):
                     UpperCamelCase       			=		pred.label_ids
                     UpperCamelCase       			=		pred.predictions

                     # all unnecessary tokens are removed
                     UpperCamelCase       			=		tokenizer.batch_decode(UpperCAmelCase_							,   skip_special_tokens=UpperCAmelCase_      )
                     UpperCamelCase       			=		tokenizer.batch_decode(UpperCAmelCase_							,   skip_special_tokens=UpperCAmelCase_      )

                     UpperCamelCase       			=		sum([int(pred_str[i] == label_str[i]      ) for i in range(len(UpperCAmelCase_      )      )]      ) / len(UpperCAmelCase_      )

                     return {"accuracy": accuracy}

              # map train dataset
              UpperCamelCase       			=		train_dataset.map(
                  _map_to_encoder_decoder_inputs							,   batched=UpperCAmelCase_							,   batch_size=UpperCAmelCase_							,   remove_columns=["article", "highlights"]							,   )
              train_dataset.set_format(
                  type="torch"							,   columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]							,   )

              # same for validation dataset
              UpperCamelCase       			=		val_dataset.map(
                  _map_to_encoder_decoder_inputs							,   batched=UpperCAmelCase_							,   batch_size=UpperCAmelCase_							,   remove_columns=["article", "highlights"]							,   )
              val_dataset.set_format(
                  type="torch"							,   columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]							,   )

              UpperCamelCase       			=		self.get_auto_remove_tmp_dir()

              UpperCamelCase       			=		SeqaSeqTrainingArguments(
                  output_dir=UpperCAmelCase_							,   per_device_train_batch_size=UpperCAmelCase_							,   per_device_eval_batch_size=UpperCAmelCase_							,   predict_with_generate=UpperCAmelCase_							,   evaluation_strategy="steps"							,   do_train=UpperCAmelCase_							,   do_eval=UpperCAmelCase_							,   warmup_steps=0							,   eval_steps=2							,   logging_steps=2							,   )

              # instantiate trainer
              UpperCamelCase       			=		SeqaSeqTrainer(
                  model=UpperCAmelCase_							,   args=UpperCAmelCase_							,   compute_metrics=_compute_metrics							,   train_dataset=UpperCAmelCase_							,   eval_dataset=UpperCAmelCase_							,   tokenizer=UpperCAmelCase_							,   )

              # start training
              trainer.train()





